more review

ggplot etc - Yt Part 1 and Part 2 - P2 is more interesting line #197.

INFO526 Term Project

Author

Affiliation

Derek Rice

School of Information, University of Arizona

# cite: https://www.youtube.com/watch?v=h29g21z0a68&t=290s 
# just a review, should probably be more focused on maps and interactive maps, a quick review  

data("faithful")
ggplot(data = faithful,
       mapping = aes(x = eruptions, y = waiting, colour = eruptions)) +
  geom_point()

ggplot() +
  geom_point(mapping = aes(x = eruptions, y = waiting, colour = eruptions < 3),
             data = faithful)

ggplot(faithful) +
  geom_histogram(aes(x = eruptions, fill = eruptions < 3))

ggplot(faithful, aes(x = eruptions, y = waiting)) +
  geom_density_2d() +
  geom_point()

# make points sqr and slightly transparent 


ggplot(faithful) +
  geom_point(aes(x = eruptions, y = waiting), shape = 'square')

ggplot(faithful) +
  geom_point(aes(x = eruptions, y = waiting), shape = 'square', alpha = 0.5)

# colour dist. in two difference colours 
ggplot(faithful) +
  geom_histogram(aes(x = eruptions, fill = eruptions < 3.1))

# colour based on weight > 60

# view(faithful) # two para. eruptions and waiting 

ggplot(faithful) +
  geom_histogram(aes(x = eruptions, fill = waiting < 60, position = "identity", alpha = 0.5))

# add a line that separates the two point dist.
# how to draw a straight lines from a slope and intercept 

ggplot(faithful) +
  geom_point(aes(x = eruptions, y = waiting)) +
  geom_abline(slope = 15, intercept = 25, colour = "red", linewidth = 3)

view(mpg)

ggplot(mpg) +
  geom_bar(aes(x = class, fill = as.factor(cyl)), colour = "black")

mpg_counted <- mpg |>
  count(class, name = "count")

view(mpg_counted)

ggplot(mpg_counted) +
  geom_bar(aes(x = class, y = count),
           stat = 'identity') #identity is just passing the data along 

ggplot(mpg) +
  geom_density(aes(x = hwy, y = after_stat(scaled)))

ggplot(mpg) +
  geom_jitter(aes(x = class, y = hwy), width = 0.2)

ggplot(mpg) +
  geom_point(
    aes(x = displ, y = hwy, colour = class)
  ) +
  scale_colour_brewer(name = 'CLASS', type = 'qual', palette = 'Set1')

ggplot(mpg) +
  geom_point(aes(x = displ, y = hwy)) +
  scale_x_continuous(breaks = c(3,5,6)) +
  scale_y_continuous(trans = 'log10')

ggplot(mpg) +
  geom_point(aes(x = displ, y = hwy, colour = class, size = cyl)) +
  scale_colour_brewer(type = 'qual') +
  scale_size_area(breaks = c(4,5,6,8))

ggplot(mpg) +
  geom_point(aes(x = displ, y = hwy)) + 
  facet_wrap(~ class)

ggplot(mpg) +
  geom_point(aes(x = displ, y = hwy)) + 
  facet_grid(year ~ drv)

# coordinates 

ggplot(mpg) +
  geom_bar(aes(x = class)) +
  coord_polar()

ggplot(mpg) +
  geom_bar(aes(x = class)) +
  coord_polar(theta = 'y')

ggplot(mpg) +
  geom_bar(aes(x = class)) +
  coord_polar(theta = 'y') +
  expand_limits (y = 70) # set max on raidial dial 

# zoom in on data - set limits on scale 
ggplot(mpg)+ 
  geom_bar(aes(x = class)) +
  scale_y_continuous(limits = c(0,40)) #removes data 

ggplot(mpg)+ 
  geom_bar(aes(x = class)) +
  coord_cartesian(ylim = c(0,40)) #coord_cartesian does NOT remove data 

# then work on theme but nothing new 

# cite: https://www.youtube.com/watch?v=0m4yywqNPVY&t=296s

# Part 2

# ggplot
## 47 geoms
## 25 stats
## 62 scales 

p1 <- ggplot(msleep) +
  geom_boxplot(aes(x = sleep_total, y = vore, fill = vore))

p2 <- ggplot(msleep) +
  geom_bar(aes(y = vore, fill = vore))

p3 <- ggplot(msleep) +
  geom_point(aes(x = bodywt,
                 y = sleep_total,
                 colour = vore)) +
  scale_x_log10()

p3

combined_plot <- p1 + p2 + p3

print(combined_plot)

plot_all <- (p1 | p2) / p3

print(plot_all)

new_plot <- plot_all

new_plot &
   theme(
    legend.position = "none" # removes legend from all plots
  ) 

new_plot +
   plot_annotation(
    title = "Mammalian Sleep Patterns",
    tag_levels = "A" # tags plots - can use roman I 
  ) &  # must use & and not +
   theme(
    legend.position = "none" # removes legend from all plots
  ) 

new_plot +
   plot_annotation(
    title = "Mammalian Sleep Patterns",
    tag_levels = "I" # tags plots - can use roman I 
  ) &  # must use & and not +
   theme(
    legend.position = "none" # removes legend from all plots
  ) 

# Plot Composition

# stories are told with multiple plots

# plot layout hsould be flexible and well aligned 

# many approaches
## facet hacking
## gridExtra::grid.arrange()
## ggpubr::ggrange()
## cowplot::plot_grid()

## patchwork <- preferred? 

p = ggplot(mtcars) +
  geom_point(aes(x = disp, y = mpg))

p + p + p + plot_layout(width = c(1,2,1))

p + p + p + plot_layout(width = unit(c(5,1,1),c('cm','null','null'))) # null is fill out remaining area

p1 <- ggplot(mtcars[mtcars$gear == 3,]) +
  geom_point(aes(x = disp, y = mpg))

p2 <- ggplot(mtcars[mtcars$gear == 4,]) +
  geom_point(aes(x = disp, y = mpg))

p1 + p2  # axis are not aligned

# view(mtcars)

p1 + p2 & scale_y_continuous(limits = c(10,40)) & scale_x_continuous(limits = c(50,500))

# how should the textual rep. be understood? 

### layout in Patchwork ### 

p1 <- ggplot(mtcars) +
  geom_point(aes(x = disp, y = mpg))

p2 <- ggplot(mtcars) +
  geom_bar(aes(x = factor(gear)))

p3 <- ggplot(mtcars) +
  geom_boxplot(aes(x = factor(gear), y = mpg))

p1 + p2 + p3

layout <- '
AAA #fraction of screen width used for plot AA# = left 2/3 and #AA = right 2/3 and AAA = full width
#BB # can also use numbers 1, 2, 3 ... 
C##
'

# p1 + p2 + p3 + plot_layout(design = layout)

### animations 

# ANIMATION 
# story telling and attention grabbing
# may turn boring people off
# our eyes are drawn to movement and our mind thinks in motion 

# many ways to make animaltions - gganimage may be the only one doing the "grammar" way 

# ggplot2 wrapper - simply provides new grammar elements and takes over rendering 

ggplot(economics) +
  geom_line(aes(x = date, y = unemploy))

ggplot(economics) +
  geom_line(aes(x = date, y = unemploy)) +
  transition_reveal(along = date)


ggplot(mpg) +
  geom_bar(aes(x = factor(cyl))) +
  labs(title = "Number of cars in {closest_state} by number of cylinders") +
  transition_states(states = year) +
  enter_grow() +
  exit_fade() 


ggplot(mpg) +
  geom_point(aes(x = displ, y = hwy)) +
  ggtitle("Cars with {closest_state} cylinders") +
  transition_states(factor(cyl))

ggplot(mpg) +
  geom_point(aes(x = displ, y = hwy, group = seq_len(nrow(mpg)))) + #define discrete groups with group=seq_len()
  ggtitle("Cars with {closest_state} cylinders") +
  transition_states(factor(cyl))

## ANNOTATION 
# two key packages
# ggrepel
# ggforce 

ggplot(mtcars, aes(x = disp, y = mpg)) +
  geom_point() +
  geom_text_repel(
    aes(label = row.names(mtcars))
  )

# ggrepel
ggplot(mtcars, aes(x = disp, y = mpg)) +
  geom_point() +
  geom_text_repel(
    aes(label = row.names(mtcars))
  )

# ggforce
ggplot(mtcars, aes(x = disp, y = mpg)) +
  geom_point() +
  geom_mark_ellipse(  # there are other options to ellipse, circle, square, hull
    aes(filter = gear == 4,
        label = '4 gear cars',
        description = "cars with fewer gears .. "))

# reduce number of points with labels
# ggrepel
mtcars2 <- mtcars
mtcars2$label <- rownames(mtcars2)
points_to_label <- sample(nrow(mtcars),10) #first ten labels 

ggplot(mtcars2, aes(x = disp, y = mpg)) +
  geom_point() +
  geom_text_repel(aes(label = label), data = mtcars2[points_to_label,])

ggplot(mtcars, aes( x = disp, y = mpg)) +
  geom_point() +
  geom_mark_ellipse(aes(fill = factor(gear), label = gear)) # very interesting 

# also a package called gghighlight 

### Part 3: Beyond GGPLOT2 #######
# https://www.youtube.com/watch?v=0m4yywqNPVY&t=3884s
# 1:24:00

# NETWORKS
# GGally
# ggnetwork
# geomnet
# ggtree
# ggdag
#install.packages("BiocManager")
#BiocManager::install("ggtree")

# install.packages("ggtree")
library(ggtree)
library(ape)

# for graphically displaying socal networks, the interelatinship between nodes

# can plot a hierarchical clusters 

iris_clust <- hclust(dist(iris[,1:4]))

iris_phylo <- as.phylo(iris_clust)

ggtree(iris_phylo) +
  geom_tiplab() +
  theme_tree2()

# Part 4 - drawing anything
# 1:48:00